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پیشبینی ظرفیت برشی تیرهای بتن مسلح با استفاده از الگوریتمهای رگرسیون بردار پشتیبان و سیستم استنتاج تطبیقی فازی-عصبی بهینه شده با الگوریتمهای فرا ابتکاری | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 6، دوره 53، شماره 8، آبان 1400، صفحه 3261-3278 اصل مقاله (1.16 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.17798.6679 | ||
| نویسندگان | ||
| فرناز اسفندنیا1؛ محمدی زاده محمدی زاده* 2 | ||
| 1گروه مهندسی عمران، دانشکده فنی و مهندسی، دانشگاه هرمزگان، بندرعباس، ایران | ||
| 2استادیار | ||
| چکیده | ||
| با توجه به پیچیدگی مکانیزمهای برشی تیرهای بتن مسلح و تأثیرگذاری پارامترهای مختلف، ایجاد یک مدل کلی جهت تخمین دقیق ظرفیت برشی، دشوار میباشد. همچنین اکثر دستورالعملهای تعریف شده برای تعیین ظرفیت برشی تیرهای بتن مسلح در آیین‌نامه‌های طراحی به صورت تجربی بدست آمده است. در سالهای اخیر الگوریتمهای هوش مصنوعی در این زمینه بسیار مورد استفاده واقع شده است. در این مطالعه از الگوریتمهای رگرسیون بردار پشتیبان و سیستم استنتاج تطبیقی فازی-عصبی بهینه شده با دو الگوریتم انبوه ذرات و الگوریتم ژنتیک برای پیشبینی ظرفیت برشی تیرهای بتن مسلح استفاده شده است. در این الگوریتم‌ها، مقادیر 9 پارامتر تأثیر‌گذار در ظرفیت برشی به عنوان ورودی و ظرفیت برشی تیرهای بتن مسلح به عنوان پارامتر خروجی مورد استفاده قرار گرفته است. با استفاده از روش اعتبارسنجی Kfold، دادههای آموزشی و تستی تعریف شده و بر اساس این دادهها پیشبینی صورت گرفته است. نتایج بدست آمده از پیشبینی نشان داد که مدل سیستم استنتاج فازی عصبی با الگوریتم بهینه‌سازی ژنتیک با ریشه دوم میانگین مربعات خطا برابر 0/06634 و ضریب همبستگی0/996 نسبت به سایر الگوریتم‌ها از دقت بالاتری برخوردار است. همچنین جهت تعیین حساسیت پارامتری متغیرهای مورد بررسی بر روی ظرفیت برشی تیرهای بتن مسلح از تئوری سیستم خاکستری (GST) استفاده شد. بررسی نتایج حاصل از این آنالیز نشان می‌دهد که میانگین ضریب آنالیز حساسیت پارامتر درصد آرماتورهای طولی ( ) نسبت به سایر پارامترها بزرگ‌تر است که نشان از تأثیر بیشتر پارامتر درصد آرماتورهای طولی بر روی ظرفیت برشی دارد. | ||
| کلیدواژه‌ها | ||
| ظرفیت برشی؛ تیر بتن مسلح؛ GST؛ PANFIS؛ GANFIS؛ SVR | ||
| موضوعات | ||
| شبکه های عصبی | ||
| عنوان مقاله [English] | ||
| Prediction of Shear Capacity of Reinforced Concrete Beams using Support Vector Regression and Adaptive Neuro-Fuzzy Inference Algorithms Optimized with Meta-Heuristic Algorithms | ||
| نویسندگان [English] | ||
| Farnaz Esfandnia1؛ | ||
| 1Department of Civil Engineering, Faculty of Technical and Engineering, University of Hormozgan, Bandar Abbas, Iran | ||
| چکیده [English] | ||
| Considering the complexity of shear mechanisms of reinforced concrete beams and the effects of various parameters, creating a general model for the accurate estimation of the shear capacity is difficult. In addition, most guidelines for the determination of the shear capacity of reinforced concrete beams in empirical design codes have been obtained experimentally. Artificial intelligence algorithms have been widely used in this area in recent years. In this study, SVR, PANFIS, and GANFIS algorithms were used to predict the shear capacity of reinforced concrete beams. In this regard, the data of 175 experimental RC beam samples were collected. In these algorithms, values of nine parameters affecting shear capacity were used as the input parameter and the shear capacity of the reinforced concrete beams as the output parameter. Using the Kfold validation method, training and test data were defined, and the predictions were performed accordingly. The results of predictions showed that the neuro-fuzzy inference system model with the genetic optimization algorithm had a higher accuracy than other algorithms with a second root mean square error of 0.06634 and a correlation coefficient of 0.996. Also, the grey system theory was used to determine the parametric sensitivity of the study variables on the shear capacity of reinforced concrete beams. The results showed that the mean coefficient of sensitivity analysis of the longitudinal rebar percentage parameter is greater than other parameters, indicating that the longitudinal rebar percentage parameter had more effects on shear capacity. | ||
| کلیدواژه‌ها [English] | ||
| Shear capacity, Reinforced concrete beam, GST, PANFIS, GANFIS, SVR | ||
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| مراجع | ||
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